Recommender systems are a relatively well-known technology today and used in different services for recommending, among other things, media items such as movies, music and pictures. Examples of such recommender systems are utilized by companies having Internet sites such as www.amazon.com and www.lastfm.com. Recommender systems help a user in finding interesting items without the user having to explicitly state what he or she wants.
A commonly used recommender method is CF (Collaborative Filtering) which produces recommendations by computing the similarity between users or items based on consumption history. CF systems suffer from the so-called ‘new user problem’, and in addition to this, they also suffer from the so-called ‘new item problem’. The new user problem means that a user has to rate a sufficient number of items before the user's preferences can be understood. The new item problem causes new items to be ignored, i.e. not recommended, until a substantial number of users have rated the item. Another well-known recommender method is CB (Content Based) recommender systems. Simply said, CB recommendations are based on the description, meta data, about the content as such. From a user's profile in terms of item consumption, the user's preferences in terms of item attributes may be derived and used to find similar items to recommend. CB systems generally also suffer from the new user problem.
Combinations of the above methods are also common and referred to as HRS (Hybrid Recommender Systems). These hybrid systems can have four different architectures:                implemented separately and combining predictions,        incorporating some content-based characteristics into a CF algorithm,        incorporating some CF characteristics into a CB algorithm, and        a unified model which incorporates both CB and CF algorithms.        
For more information see Y-L. Chen and L-C. Cheng: A novel collaborative filtering approach for recommending ranked items, Expert Syst. Appl., 34(4):2396-2405, 2008.
CA (Context Aware) recommender methods have emerged in the past years as the use of location-aware devices with various sensors have become more popular. Recommending applications for mobile devices has for instance been done by presenting which applications other users geographically close to the active user are consuming. This has been discussed in A. Girardello and F. Michahelles: AppAware: Which Mobile Applications Are Hot?, MobileHCI, 10 Sep. 7-10, 2010.
There are a number of different recommender systems entering the mobile device area making use of the location context of the device. Patent application US-2006/0266830-A1 discloses a method where location is used to enhance scaling of CF. The method requires that each location has a sufficient amount of consumption data for another recommender technique to produce good recommendations on the subset of all consumptions. Patent application US-2009/0193099-A1 discloses a system that assess a hypothetical context, such as current context, future times and future location, based on the context of each prior user request. The hypothetical context is then used to produce recommendations for the user. Recommendations are thus based on where a user has been as context and maps that to a neighborhood context. Hypothetical contexts here do however not make use of other data than what is known for the current user and the system does not capture any characteristic of the items to be recommended in relation to context.
The context-aware approach, using the context of the user requesting recommendations and the context of previous item consumptions in the system to produce recommendations introduce a new problem. To recommend, for example, items popular or frequently consumed in a certain location, such item data must already be present in the system and if a user is among the first to visit this location, there will be no or little data present.